Modeling and parameter identification approach for power battery pack used in electric vehicle

The online and offline parameter identification approaches were built with extended Kalman filter (EKF) and the least square algorithm respectively based on the proposed electro-chemical polarization (ECP) model for the lithium-ion power battery pack used in the electric vehicle. Validation results based on the hybrid pulse power characterization test showed that the parameter identification approaches with the ECP model can ensure the maximum relative error within 1% and accurately simulate the dynamic voltage behavior of the power battery pack. By using the proposed parameter identification approaches, the operating efficiency of the battery management system can be greatly improved becacuse the time-consuming, laborious, even error-prone experiments and periodical calibration for model parameters before the first operation of the battery pack are avoided effectively.